Title :
A neural network controller for a temperature control system
Author :
Khalid, Marzuki ; Omatu, Sigeru
Author_Institution :
Dept. of Inf. Sci. & Intelligent Syst., Tokushima Univ., Japan
fDate :
6/1/1992 12:00:00 AM
Abstract :
A backpropagation neural network is trained to learn the inverse dynamics model of a temperature control system and then configured as a direct controller to the process. The ability of the neural network to learn the inverse model of the process plant is based on input vectors with no a priori knowledge regarding dynamics. Based on these characteristics, the neural network is compared to a conventional proportional-plus-integral (PI) controller. Experimental results show that the neural network controller performs very well and offers worthwhile advantages.<>
Keywords :
controllers; learning systems; neural nets; temperature control; backpropagation; inverse dynamics model; learning systems; neural network controller; process plant; temperature control; Adaptive control; Backpropagation algorithms; Control systems; Intelligent control; Inverse problems; Jacobian matrices; Neural networks; Process control; Stability; Temperature control;
Journal_Title :
Control Systems, IEEE
